Review Journal

The Gerontologist
Cite journal as: The Gerontologist Vol. 54, No. 5, 818–829
doi:10.1093/geront/gnt074 Advance Access publication July 30, 2013

Suitability of Public Use Secondary Data Sets
to Study Multiple Activities

Michelle Putnam, PhD,*,1 Nancy Morrow-Howell, PhD,2 Megumi Inoue, MSW,3
Jennifer C. Greenfield, PhD,4 Huajuan Chen, MSW,4 and YungSoo Lee, PhD5

1School of Social Work, Simmons College, Boston, Massachusetts.
2Brown School of Social Work and Center for Aging, Washington University in St. Louis, Missouri.
3Graduate School of Social Work, Boston College, Chestnut Hill, Massachusetts.
4Brown School of Social Work, Washington University in St. Louis, St. Louis, Missouri.
5Department of Social Welfare, University of Incheon, Yeonsu-gu, Incheon, Korea.


*Address correspondence to Michelle Putnam, PhD, Simmons College, School of Social Work, 300 The Fenway, Boston, MA 02135.
E-mail: michelle.putnam@simmons.edu

Received February 19, 2013; Accepted June 12, 2013
Decision Editor: Nancy Schoenberg, PhD


Purpose of the Study: The aims of this study
were to inventory activity items within and across

U.S. public use data sets, to identify gaps in represented
activity domains and challenges in interpreting
domains, and to assess the potential for studying
multiple activity engagement among older adults
using existing data. Design and Methods: We
engaged in content analysis of activity measures
of 5U.S. public use data sets with nationally
representative samples of older adults. Data sets
included the Health & Retirement Survey (HRS),
Americans’ Changing Lives Survey (ACL), Midlife
in the United States Survey (MIDUS), the National
Health Interview Survey (NHIS), and the Panel Study
of Income Dynamics survey (PSID). Two waves of
each data set were analyzed. Results: We identified
13 distinct activity domains across the 5 data
sets, with substantial differences in representation of
those domains among the data sets, and variance in
the number and type of activity measures included
in each. Implications: Our findings indicate
that although it is possible to study multiple activity
engagement within existing data sets, fuller sets
of activity measures need to be developed in order
to evaluate the portfolio of activities older adults
engage in and the relationship of these portfolios to
health and wellness outcomes. Importantly, clearer
conceptual models of activity broadly conceived are
required to guide this work.
Key words: Activity, Secondary data sets, Measurement,
Content analysis

Scientific interest in activity engagement by
older adults has expanded in recent years as
researchers have sought to better understand the
range of activities older adults engage in and the
impact of activity engagement on outcomes such
as health and wellness, quality of life, and life satisfaction.
However, despite the increased scholarship
in the area of activity, there has been less attention
to defining what activity means conceptually. This
includes determining what counts as an activity—
for example, is it anything a person does with his
or her time? Additionally, the measurement of different
types of activities (e.g., physical, social, psychological,
and economic) and knowledge about
how to use multiple activity variables in statistical
models are both underdeveloped. In general,
researchers target a single domain of activity, like
physical activity, volunteering, caregiving, employment,
or social activities, despite the fact that older
adults can and do engage in multiple activities on a
daily basis and at any one time. Research also tends
to exclude categories of time use that have not traditionally
been thought of as activities, but that
can consume significant amounts of an individual’s
time, such as attending medical appointments and

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818 The Gerontologist


engaging in household chores. Moreover, the relevance
of intensity of participation—for example,
the difference between involvement and engagement
in activities and roles (James, Besen, Matz-
Costa, & Pitt-Catsouphes, 2012)—is not always
considered in analysis of activities, yet may be
important for understanding how individuals balance
engagement in multiple activities.

Improving the ability of researchers to study
multiple activity engagement can help to generate
greater understanding that helps both to clarify the
complex relationships between activity and health
and wellness outcomes and to better define activity
itself. To help move this work forward, we undertook
an informed assessment of what our current
data resources are for studying multiple activities.
We present our study findings here.

Rationale for Assessing Data Resources for
Studying Activity

Moving toward understanding activity in a
more complex way is supported by continued calls
to facilitate healthy and positive aging through
public health initiatives and evidence-based practices
(U.S. Department of Health & Human
Services, 2012; United Nations, 2002; WHO,
2007). The growing body of research literature on
specific types of activities have included studies on
social engagement, work, caregiving, volunteering,
religious activity, leisure, and physical activity
and their relationships to health, cognitive function,
functional status, and mortality (Buchman,
Wilson, & Bennett, 2008; Glass, De Leon, Bassuk,
& Berkman, 2006; Hong & Morrow-Howell,
2010; Janke, Payne, & Van Puymbroek, 2008;
Karp et al., 2006). In general, activity participation
has been linked to positive outcomes (Chipperfield,
2008; Hong & Morrow-Howell, 2010). However,
certain activities, like caregiving or employment
under certain conditions, have been associated with
negative outcomes (Adams, McClendon, & Smyth,
2008; Son et al., 2007). In several studies, older
adults themselves have expressed the idea that
engagement in activities, from personal hobbies to
productive work roles, is vital in the pursuit of a
good old age (Bowling & Gabriel, 2007; Clarke,
Liu-Ambrose, Zyla, McKay, & Khan, 2005).

Researchers have found that motivation
(Holahan & Suzuki, 2005), higher levels of perceived
control, self-esteem, efficacy (Bailis, Chipperfield,
& Helgason, 2008), and social support (Wilson &
Spink, 2006) are all important positive correlates of

individual activity engagement, as are the contexts
in which activities are undertaken, including neighborhood
characteristics (Mendes de Leon et al.,
2009). Lower levels of formal education (Shaw
& Spokane, 2008), presence of disease diagnosis
(Ashe, Miller, Eng, & Noreau, 2009), and greater
fear of falling (Deshpande et al., 2008) have also
been related to reduced activity engagement.

Within the body of research on activity and
older adults, measurement of activity contains tremendous
variation, due in part to the lack of strong
conceptual models of activity that are broadly
defined. In our review of the public health, gerontology,
rehabilitation, and related literatures, we identified
three main issues that stymie the development
of knowledge related to activity. First, operationalization
of specific activity domains is inconsistent
across studies. For example, physical activity has
been evaluated as a specific single item, like walking
(Nagel, Carlson, Bosworth, & Michael, 2008),
and as a composite ordinal scale of physical activities
that comprise basic activities of daily living
(Peri et al., 2008). Second, data collection methods
vary widely depending on the aims of the study,
producing findings that are difficult to compare or
assess. Staying with physical activity, data collection
modalities range from self-report in activity
diaries (Atienza, Oliveira, Fogg, & King, 2006) to
actigraphs (Chipperfield, 2008) and accelerometers
(Bailis, Chipperfield, Perry, Newall, & Haynes,
2008), which electronically track physical activity.
Third, single activities or domains, like physical
activity, are generally not reviewed within the context
of larger activity patterns, making it difficult
to understand how participation in a given activity
domain relates to participation in other domains
(e.g., physical activity, volunteering, providing
social support, and work) or how such participation
collectively supports larger health and wellness
outcomes. Of the few studies we found that considered
multiple activity engagement, results indicated
that when a broad set of activity items were reduced
into composite domains, patterns in activity were
evident (Burr, Mutchler, & Caro, 2007). Using this
approach, analysis of antecedents and outcomes of
activity patterns on areas of health and wellness,
including incidence of dementia (Paillard-Borg,
Fratiglioni, Windbald, & Wang, 2009) and depression
(Arai et al., 2007) have been studied.

We believe that to efficiently and effectively
advance research on activities and older adults,
better approaches to conceptualizing, measuring,
and analyzing activities must be developed. The

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projected scope of this work is large and involves
several components, including (a) improving the
conceptual specification of activity, (b) identifying
existing measures of activity, (c) assessing measurement
properties, and (d) evaluating the use of a
collection of activity measures to investigate multiple
activity engagement. The analysis we present in
this article addresses the second component listed
earlier, completing a partial inventory of activity
measures available. We premised our work on a
broad model of activity and contributors to activity,
as described subsequently.

Theoretical Context for Understanding Activity
Engagement

A broad look at nearly 50 years of theoretical
work on activity and aging shows that interest in
activity has fluctuated over time and has somewhat
paralleled pathways of empirical research.
Havinghurst (1957) first formalized Activity
Theory in 1957, and since then, activity has been
included either directly or indirectly in a host of
significant conceptual models of aging. A recent
and global framework of activity receiving widespread
attention is the WHO’s (2007) model of
Active Aging, the centerpiece of its agenda for
healthy aging. Although it does not explicitly
address the paradigms of successful aging (Rowe
& Kahn, 1998) or productive aging (Butler &
Gleason, 1985), the WHO’s model does posit six
determinants of activity engagement—all of which
are influenced by culture and gender—that result
in active aging. There are three individual-level factors:
personal, behavioral, and social determinants;
this is consistent with socioemotional selectivity
theory (Carstensen, 1992; Hendricks & Cutler,
2004). Also, there are three structural-level factors—
the physical environment, economics, and
health and social services—that align with public
health frameworks, like the social model of disability
put forth in the International Classification of
Function (WHO, 2007). “Active ageing” is defined
as the optimization of “opportunities for health,
participation and security in order to enhance quality
of life as people age” (WHO, 2007). The Active
Ageing Framework guides WHO’s global age-
friendly communities initiative and is based upon
the United Nation’s 2002 Madrid International
Plan of Action on Ageing (United Nations, 2002).

We propose that the concept of active aging implies
engagement in multiple domains of activities simultaneously,
which requires greater understanding of

a person’s activity portfolio. We distill the idea of an
activity portfolio from Birren and Feldman’s (1997)
larger concept of a life portfolio, a tool for individuals
to plan and review life investments. In its
broadest sense, an activity portfolio would be composed
of things a person does or spends time doing,
ranging from sleeping to thinking to exercising. In a
more focused approach, an activity portfolio might
have domains that are evaluated for their relevance
in understanding health and wellness outcomes.

Our ability to assess multiple activity engagement
and activity portfolios using existing data
sets, however, is limited to the availability of activity
measures in these data sets. To begin the process
of determining the range and scope of measures
available to researchers, we completed a content
analysis of activity measures in five U.S.-based
public use data sets commonly used to study older
adults. Our aims were to inventory discrete activity
measures and group them into conceptual
domains representing areas of activity engagement.
Through this qualitative review, we intended
to identify potential gaps in activity domains, to
understand challenges in interpreting measurement
domains, and to assess data set potential for
advancing research on activity and activity patterns
and portfolios among older adults.

Design and Methods

Our analysis was guided by these research aims:

(a) To determine the number of activity-related
variables in the sample of secondary data sets, (b)
to determine for each data set whether discrete
activity variables can be grouped into cogent conceptual
domains, (c) to identify gaps in activity
domains and challenges in interpreting domains,
and (d) to determine which of the data sets assessed
seemed most appropriate for pursuing analysis of
activity patterns and portfolios in future research
on activity and older adults.
Sample

We selected public use data sets readily available
and regularly used by gerontology researchers.
Criteria for initial data set selection were: self-report
survey, nationally representative population of the
United States, inclusion of older adults (65+), and
survey content covering at least one area of activity
recognized in the literature (e.g., physical activity
and volunteering). Our initial sample included nine
data sets: the Health and Retirement Study (HRS),

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820 The Gerontologist


Americans’ Changing Lives Study (ACL), Midlife
in the United States (MIDUS), National Health
Interview Survey (NHIS), Panel Study of Income
Dynamics survey (PSID), American Time Use Survey
(ATUS), Behavioral Risk Factor Surveillance System
(BRFSS), American Community Survey (ACS), and
Longitudinal Studies of Aging (LSOAs). From this
sample, we chose five longitudinal data sets (HRS,
ACL, MIDUS, NHIS, and PSID) that contained
similar measures of physical and emotional health
for purposes of modeling outcomes of activity participation.
Descriptions of these are presented in
Table 1. Three data sets (ATUS, ACS, and BRFSS)
were excluded because data are cross-sectional only.
LSOAs were excluded because the sample starts at
age 70 and limits assessment of activity at younger
ages. To meet our research aims, we reviewed the
two most recent waves of data collected at the time
of our analysis. Earlier waves of some data sets may
be reviewed in future analyses to better understand
longitudinal patterns of activity engagement.

Measures

For the purposes of this study, we intentionally
defined activity broadly, and we did not select
measures using an existing theory or model of
activity. Instead, we wanted to identify the widest
universe of activity measures without prejudice and
to determine a data set’s potential to inform subsequent
analyses of multiple activity engagement.
Although we understand the relevance of existing
conceptual schematics of participation to the
project, including the International Classification
of Function (WHO, 2001), we determined not to
make this link for this analysis.

Based on this approach, we devised a simple test
to identify an activity measure. First, we determined
whether the intent of a survey item was to inquire
about “doing” something, regardless of how it
was phrased. This is in comparison to inquiring
about feeling, thinking, believing, having, getting
help with, or similar question stems. Second, if it
seemed a survey item was about “doing,” we tested
our assessment by rephrasing the survey question
to see if it was possible to reword it as “do/did you
do X” or “how much/often do you do X?” If it was
not possible to rephrase the measure into either of
these two ways, we determined it was not an activity
measure. Third, we reviewed the time period of
the activity. If the question was about whether an
activity was done or not within the standard look-
back range of the survey (e.g., the HRS inquires

about activities done the year of the survey and the
year prior), we included it. If the question asked
“did you ever do X. . .,” but did not ask when that
activity was done, we logged it in our notes but
excluded it from the analysis because we could not
assign it a specific temporal frame that would permit
it to be analyzed as part of an activity portfolio
that included measures with the same look-back
period. Measures that asked about the experience
of doing the activity (e.g., where/when the activity
is done, effort required to complete the activity, or
any other description of the activity or the context
within which it was performed) were also noted in
our logs but not included in the analysis. In sum,
inclusion criteria required that it was possible to
recode the measure into either a binary or ordinal
variable that could be empirically assessed to indicate
whether a person was doing an activity or not.

Data Collection and Analysis

We selected content analysis as a method of data
collection and analysis because of its suitability for
reviewing discrete items systematically and reporting
findings of counts, categories, and subdomains
(Krippendorff, 2004; Neuendorf, 2002). Data collection
involved identifying all activity items within
the five data sets (ACL, 1994, 2002; HRS, 2007,
2009; MIDUS, 1994, 2004; NHIS, 2009, 2010;
PSID, 2007, 2009) and data collection waves listed
in Table 1. Data analysis involved sorting measures
into categories and generating descriptive statistics
(Elo & Kyngäs, 2008). Reliability was based
on intercoder agreement (Burla et al., 2008). Our
analysis met four primary standards of rigorous
content analysis including purposive sampling of
a defined population (publicly available secondary
data sets with nationally representative samples
of older adults), variable selection based on past
research or theory (activity domains and measures),
defined medium of review (electronic files),
specific research aims (Aims 1 and 2 above), and
operationalized definitions of critical analysis variables
(activity measures are defined in Measures
section earlier).

In Part 1 of the content analysis, two reviewers
compiled lists of all activity-related variables in
each wave of the data set under review and then
grouped them into general domains according to
like variables. They assigned each survey item to
only a single domain. For survey items with a single
root question, all subquestions (e.g., 20, 20a, 20b,
20c) were reviewed as individual variables. Only

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Vol. 54, No. 5, 2014 821


Table 1. Summary of National Survey Data Sets Evaluated

Data set and survey purpose Data waves collected Data collection Sample age parameters Sample size and unit

822 The Gerontologist

ACL

Explores the role of psychosocialand economic factors over theadult life course in health andfunction outcomes.

HRS

Explores changes in labor forceparticipation and healthtransitions toward the end ofwork lives and after.

MIDUS

Investigates social, behavioral,
and psychological factors inaccounting for age-relatedvariations in health andwell-being.

NHIS

Monitors U.S. population health.

PSID

Explores socioeconomics and healthover the life course and acrossgenerations.

Panel data: 1986, 1989, 1994,2002 (study closed)

Panel data: Biannual 1992–2010(ongoing) Additional off-yearstudies including activitysupplements.

Panel data: 1995–962004–2006 (Wave III pending*)

Cross-sectional, annual:
1957–2012 (ongoing)

Panel data: 37 Waves 1968–2011(ongoing)

Face-to-face interviews

Face-to-face interviews

Telephone interview,
mailed questionnaire

Face-to-face interviews

Face-to-face andtelephone interviews,
social security, and
census data

Age stratified: 25 years and older

Age stratified: 50 years and older

Age stratified: 25–74 years

Age stratified: children andadults

Age stratified: children andadults.

Individuals:

1986: 3,617

1989: 2,867

1994: 2,562

2002: 3,617

Varies by year. In 2010,15,372 individuals, 10,754
households.

Individuals:
1995–1996: 7,1082004–2006: 4,963

Varies by year. Estimated
in 2011 to be 87,500individuals, 35,000
households.

Varies by year. In 2009, more
than 24,000+ individualsand 8,500+ families.

Note: *MIDUS Refresher data collection in 2012 will replenish the sample with new members; MIDUS III data collection begins in 2013.

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questions pertaining to the individual respondent
(i.e., “did you. . .”) were counted; those that asked
the respondent to provide a proxy respondent for
a spouse or other individuals were not evaluated.
Thus, within surveys that collect data on multiple
members in a household, like the PSID, we examined
only items related to the primary respondent to
standardize comparisons across surveys. In surveys
with skip patterns (e.g., “if yes, go to. . .” and “if no,
skip to. . .”), we counted all questions in the “yes”
pattern in order to capture all of the activity measures
under a single question line. Surveys that sought
to validate responses by asking multiple questions
about the same activity were noted in our data files,
but these items were not multiply counted. In the
age-stratified data sets, we did not select any questions
related to activity that were asked of younger
adults but not asked of older adults.

In Part 2, findings from each data set were
reviewed in a series of conference calls with four

researchers and discussed until agreement was
reached regarding inclusion or exclusion of activity
items for each wave. The researchers simultaneously
engaged in review of categorization of individual
items into conceptual domains. During this review
process, we recorded decision rules for identification
of activity items and definitions of categories and
established guidelines for categorizing measures into
conceptual domains. We added and further refined
rules throughout the process. After a final rule book
was established, we retroactively applied all rules to
each wave of each data set and completed a comparative
review of its application by two researchers.
Any discrepancies in application of the final rule set
were discussed until agreement was reached.

Results

Table 2 presents the 13 domains identified in the
analysis and their definitions. Findings for research

Table 2. Activity Domain Names and Definitions Schematic

Activity domain name Domain definition

A. Employment activities Activities related to paid work, full or part time.
B. Health risk behavior activities Activities that increase risk of disease or injury (e.g., smoking, alcohol consumption,
and drug use).
C. Basic living activities Activities that are routine and related to regular function (e.g., sleep, grooming, and
sexual engagement).
D. Civic activities Activities that require individuals to actively participate in formally organized events,
meetings, programs, or events (e.g., volunteering and going to meetings or clubs).
E. Leisure activities Activities done by choice during an individual’s free or discretionary time (e.g., reading,
watching TV, listening to music, dining out, and attending lectures). In this analysis,
leisure does not include activities specified in other domains, like physical activity,
although some people may view these as leisure.
F. Household chore activities Activities done to as part of personal and household administration, maintenance,
and improvement (e.g., preparing meals, cleaning the home, taking care of pets,
maintaining the yard, and fixing an automobile).
G. Helping others’ activities Activities that have, as their main purpose, providing informal assistance to other
individuals including family members, friends, and neighbors (e.g., providing
emotional support, running errands for others, and providing assistance with
household chores or transportation).
H. Religious activities Activities that are related to religious engagement (e.g., attending a service, praying, or
meditating).
I. Interpersonal exchange Activities that involve person-to-person contact as the primary mode of the action (e.g.,
activities visiting neighbors, telephone conversations, showing affection, and e-mailing friends
or family).
J. Help-seeking activities Activities related to obtaining assistance or support for physical or mental health or
other care needs (e.g., going to see a doctor, attending a support group, seeking
professional assistance in a community or hospital setting).
K. Physical exercise activities Activities related to physical exercise (e.g., walking, participating in sports, gardening,
and light housework).
L. Financial management Activities related to household fiscal administration and personal money management
activities (e.g., paying bills, managing financial accounts, and managing medical expenses).
M. Computer activities Activities related to general computer use (e.g., sending e-mails and searching the
internet) that are not otherwise identified as having an explicit purpose, such as
e-mailing a doctor or a family member.

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aims 1 and 2 are reported in Table 3, which presents
numerical counts of measures by domain
and data set wave. The number of activity measures
identified across the five data sets ranged from
39 (HRS 2009) to 109 (MIDUS 1994, 2004). The
two data sets with the most domains were MIDUS
(n = 12) and HRS (n = 12). Employment/paid work
and physical activity were the only domains present
in all five data sets. Health risk behavior activities
were present in four of the five data sets.

The number of measures that are the same across
waves of a data set is also reported by domain in
Table 3. In most cases, the survey measures across
waves are identical. In others, the wording may be
slightly different (e.g., ACL 1994: “Including paid
vacations and sick leave, how many weeks altogether
were you employed during the past 12 months?”
and ACL 2002: “How many weeks altogether were
you employed during the past 12 months, including
paid vacations and sick leave?”). Survey items
that address similar concepts but either have distinctly
different wording that changes the focus
of the question or include different examples of
activities were not counted as being the same (e.g.,
MIDUS 1994/95: “During the summer, how often
do you engage in moderate physical activity [e.g.,
bowling or using a vacuum cleaner?]” and MIDUS
2004–2006: “How often do you engage in moderate
physical activity, that is not physically exhausting,
but it causes your heart rate to increase slightly
and you typically work up a sweat? [Examples leisurely
sports like light tennis, slow or light swimming,
low-impact aerobics, or golfing without a
power cart; brisk walking, mowing the lawn with a
walking lawn mower].”). Additional details of the
data analysis not presented in this article are available
from the authors.

Limitations

Limitations for this analysis included a small
sample size and our assessment of only 2 years of
survey instruments. We recognize that our coding
of some items as activities (such as health risk
behaviors) may be questioned, but we believed it
was important to include them in this initial activity
item review. Despite these limitations, we find
that the content analysis provided the benefit of
creating a replicable example of assessing activity
items within secondary data sets that could be
applied to other surveys. It also yielded a broad
range of activity domains for consideration in
empirical work.

Discussion of Findings

Results from our analysis showed that each data
set contained multiple activity domains. Although
no data set contained all of the identified domains,
HRS and MIDUS contained most of them. Within
each data set we reviewed, the number of activity
items per domain varied. The specific activity
items within domains also differed across data sets.
Moreover, in cases where similar activities were
inquired about (i.e., volunteering), often the measures
themselves were not the same across data sets.
One reason that there might be more activity items
in a particular domain may be extensive triangulation
of information in some data sets, such as the
PSID, which rigorously measures employment so
that fluctuations in employment and employment
trends over time can be confidently investigated.
A reason that very few items may exist in a domain
may be that these activity domains were not considered
to be essential to the mission of the study. That
said, we recognize that a small number of items does
not necessarily equate to limitations in measurement,
and item quality is highly relevant to obtaining
adequate and meaningful data. Additionally, we
note that some items, such as “helping neighbors”
or “helping others manage medications,” may represent
complex activities that include social, physical,
psychological, and other components (e.g., financial
management and time spent on a computer).

Still, we found the analytical grouping of activity
measures into domains to be useful in understanding
data resources available to assess multiple
activity engagement. In the following sections, we
consider the implications of identified gaps in
domains and variance in activity items within and
across data sets, as well as challenges in interpreting
the domains. This is followed by recommendations
for this work going forward.

Gaps in Activity Domains and Variance in
Activity Items

As noted earlier, our findings resulted in the identification
of a broad set of activity domains across
the five data sets but clearly showed gaps within
and between data sets in terms domain representation
and content. The only universal domain across
surveys was employment although the number of
items within this domain that fit the “do you do”
criteria ranged from 32 in the PSID to just 2 items
in the HRS. The PSID inquires extensively about
status, nature, and time spent in employment, triangulating
data to ensure both rigor and ability

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Vol. 54, No. 5, 2014 825

Table 3. Summary of Activity Domains Found in Five National Data Sets

Data setSurvey wave and Number
number of total of activity Health risk Basic Household Helping Interpersonal Physical Financial
activity measures domains Employment behavior living Civic Leisure chore others Religious exchange Help-seeking exercise management Computer

NHIS2010 Survey of adults 6 7 9 1 1 14 5
(N = 37)
2009 Survey of adults 6 7 9 1 1 18 5
(N = 41)
Number of items same 7 9 1 1 12 5
across wavesPSID2009 Family survey 6 27 5 2 1 3
(N = 38)
2007 Family survey 6 32 5 2 3 1 3
(N = 46)
Number of items same 27 5 2 1 1
across waves

ACL2002 Survey (N = 51)97 6 8 12832 32
1994 Survey (N = 54)985 7 121114 42
Number of items same 5 4 7 12 7 1 2 2

across wavesHRS2009 Activity12 2 329832 3 2 2 2 1
supplements (N = 39)
2007 Activity12 2 329832 3 2 2 2 1
supplements (N = 39)
Number of items same 2 329 8 3 2 3 2 2 2 1
across waves

MIDUS2004–2006 (N = 109)12 14 10 4165 111 4 5 20 18 1
1994/1995 (N = 109)10 25 10 116 111 4 5 19 4
Number of items same 5 10 1 14 1 11 5 15

across waves

Note: Detailed tables with survey items can be obtained by contacting the corresponding author.

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to evaluate employment in-depth (PSID, 2009). In
contrast, the HRS asks only about current employment
status and hours spent per week working for
pay. The other surveys fall somewhere in the middle,
asking additional questions about type, nature,
and specifics of employment. Depending on what a
researcher’s aims are, variables providing dynamics
of employment may be particularly relevant
when investigating the interaction of employment
factors and other life activities. On the other
hand, if formal employment status (employed and
not employed) is the only variable of interest, the
one or two employment items may be enough. In
an empirical analysis exploring multiple activity
engagement, employment status and hours spent
working may be the most salient measures.

Civic activities and religious activities were covered
in four of the five data sets, with MIDUS having
the greatest number of items in each category
overall. In MIDUS, ACL, and HRS, items related
to civic and religious activities were similar in
what they measured (e.g., volunteering and attending
religious services). MIDUS more extensively
measured what type of organizations or population
groups individuals volunteered to work with
and collected more details regarding religious
engagement.

The fact that PSID had no civic activity items
and NHIS had only one was disappointing given
current interest in volunteering and productive
aging and their relationships to positive health
outcomes. This limits investigation of patterns
of employment and transition to or inclusion of
civic activities in retirement and their relationship
to positive health outcomes using these data sets.
The lack of religious activities in these data sets
similarly stymies researchers’ ability to investigate
relationships between health and spiritual and/or
activities like volunteering. As noted earlier, these
omissions may be attributed to the original mission
of each survey, and the survey developers,
therefore, should not be faulted for exclusion;
rather, survey developers could be encouraged to
consider the possibility of adding items in unrepresented
activity domains to facilitate research
exploring complex interactions of multiple activity
engagement.

Household chore activities were assessed by
many items in ACL and HRS, but very few in the
other data sets. In many of the physical activity
items, engagement in housework and exterior yard
work were used as examples for explaining distinctions
between light, moderate, and vigorous

physical activity and/or exercise. With the exception
of the 2004 wave of MIDUS, only a few items
in each data set inquire about physical exercise.
Given the likely empirical correlation of household
chores and physical activity, understanding how
these domains differ (if they do) becomes important
to delineate. Helping others may also link closely
to activities in both the aforementioned domains.
In creating all three of these domains (household
chores, physical activity, and helping others), our
research team felt interpreting the intent of what
each survey item was trying to assess was useful in
assigning each item to a single domain. At the same
time, we recognize that what these items measure
specifically may overlap (e.g., physical engagement),
suggesting the need for greater conceptual
and measurement work in these cases and others
of a similar nature.

Financial management and computer activity
domains are not routinely included. In each, the
items are somewhat generic (time spent managing
bills and using a computer) but do capture activities
that can consume large amounts of time. From
a similar viewpoint, help-seeking activities, present
in four of the five data sets, may range from barely
any time spent to significant time spent. In all of
the domains, fluctuations in time spent may influence
time or effort available for other domains of
activities. The larger number of items in NHIS and
MIDUS ask specifically about types of help sought
(e.g., physician, psychologist, and dentist), offering
more detail about the range of help seeking.
In contrast, the time spent using a computer, a single
item in this domain, only generically inquires
about use of the machine, not what it was used for.
In trying to asses this, our research team returned
to what became a familiar refrain for us during this
analysis: It likely depends on the specific research
aims as to whether the number of items and their
quality are strong enough to produce a meaningful
analysis of multiple activity engagement.

Difficulty in Interpreting Domains

In terms of interpreting domains, we struggled
in determining whether health risk behavior was
a legitimate domain of activity. In the literature,
health behaviors are treated distinctly from physical
and social activities. However, in our iterative
discussions, we found that we could make the case
for including health risk behavior as these activities
are sometimes done with singular focus (e.g.
taking a smoking break) or done in tandem with

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826 The Gerontologist


other activities (drinking alcohol and socializing).
Within our formula of asking “do you do X?”, we
determined health risk behavior qualified as an
activity domain, but we recognized much more
thought would be required if this domain was to be
included in a broad conceptual model of activity.

Leisure, unexpectedly, also became a troubling
domain for our research team. We determined that
only two data sets, HRS and MIDUS, include leisure
items, because most other activity items fit
into domains with sharper parameters. One person’s
leisure activity is not always another’s; personal
preference and interpretation of activity
seem important in deciding what exactly leisure is.
Similarly, within the basic activity domain, items
like sleeping, eating, and having sex fit our inclusion
criteria but may or may not be useful items in
investigating an activity profile unless put into a
specific context.

Implications for Studying Multiple Activities or
Activity Domains Simultaneously

Based on our findings, we suggest that it is
possible to categorize activity items within existing
data sets into domains and that the resulting
domains lend themselves to the study of multiple
activity engagement (activity portfolios) and
older adults. As for the question of whether or
not there are currently enough activity items
within the data sets reviewed to begin to study
activity portfolios, we would answer yes based
on the quality of the data sets and the fact that
at least two—HRS and MIDUS—contain most
of the identified domains. We offer a strong
note of caution, however, in that it is unclear
whether the best measurement of activity items
is presented in these surveys and whether enough
items exist within each domain to truly capture
what a domain might represent. Depending on
the research aims, missing domains or limited
items within each domain may be handled differently
by researchers. Despite these challenges,
next steps in this work might include assessing
the potential to create composite measures based
on activity domains and exploring the existence
of activity patterns and portfolios. Examining
activity change over time using multiple waves
of the longitudinal data sets assessed here also
holds potential for better understanding activity
engagement over the life course.

Our findings also highlight the need to better
conceptually align measures of functional

limitations (activities of daily living) and measures
of activity engagement. Arguably, the “Do
you do X?” criterion we employed in this analysis
to identify activity variables within data sets
is broad enough that it could capture some functional
capacity items. In most surveys, the intent
of functional limitation questions are differentiated
by inquiring “can you do X?” versus “do you
do X?”. However, it is possible that future work
assessing activity portfolios may help researchers
better understand the relationship of functional
limitation to multiple activity engagement among
older adults by exploring its role as an antecedent
and outcome of health and wellness across a broad
range of activity domains.

Finally, although our findings identified 13 distinct
domains in the five data sets we examined,
we believe there are likely more domains relevant
to the development of activity portfolios.
These include areas that range from more internal
activities, like thinking or self-reflection, to more
external activities like pursuing educational opportunities.
We also believe that there may be utility in
subdividing our identified domains to create more
refined categories of activity that more adequately
capture motivation or intent of activities. For
example, attending religious services may be quite
different than meditation despite the fact that both
might be viewed as spiritual. Empirical analysis of
activity domains can help determine activities that
are similar enough to become composite variables
for a single domain within a data set, but it will not
identify missing components of domains. For this,
more attention should be given to the development
of a conceptual model of activity portfolios.

Conclusion

Our analysis highlights the need for greater
attention to creating strong data resources for
studying multiple activity engagement among
older adults. Our assessment of five of the most
commonly used data sets in gerontology research
suggests that there is potential to use them in
this work, but that inclusion of a fuller range of
domains, attention to the items included within
each domain, and the measurement of those items
is essential for generating robust research findings.
Moreover, our analysis draws attention to the
lack of conceptual clarity around activity, broadly
defined, and the distinct need for development of
stronger theoretical models of activity to support
investigation of activity portfolios.

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Vol. 54, No. 5, 2014 827


Funding

This work is supported by a grant from the National Institute on
Aging (1R21AG038868-01A1).

Acknowledgments

M. Putnam was co-PI on the study leading to this article and took lead
responsibility for writing the manuscript. N. Morrow-Howell was co-PI
on the study and was fully involved in all aspects of manuscript preparation.
J. C. Greenfield and M. Inoue participated in all data analyses and
were involved in preparing results and interpretation of findings. H. Chen
and Y. S. Lee participated in data analysis and interpretation of findings.
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Review Journal


In general, researchers target a single domain of activity, like physical activity, volunteering, caregiving, employment, or social activities, despite the fact that older adults can and do engage in multiple activities on a daily basis and at any one time. Research also tends to exclude categories of time use that have not traditionally been thought of as activities, but that can consume significant amounts of an individual’s time, such as attending medical appointments and engaging in household chores. Within the body of research on activity and older adults, measurement of activity contains tremendous variation, due in part to the lack of strong conceptual models of activity that are broadly defined. In our review of the public health, gerontology, rehabilitation, and related literatures, we identified three main issues that stymie the development of knowledge related to activity.
They propose that the concept of active aging implies engagement in multiple domains of activities simultaneously, which requires greater understanding of a person’s activity portfolio. Their ability to assess multiple activity engagement and activity portfolios using existing data sets, however, is limited to the availability of activity measures in these data sets. To begin the process of determining the range and scope of measures available to researchers, we completed a content analysis of activity measures in five U.S.-based public use data sets commonly used to study older adults. Their aims were to inventory discrete activity measures and group them into conceptual domains representing areas of activity engagement. Through this qualitative review, they intended to identify potential gaps in activity domains, to understand challenges in interpreting measurement domains, and to assess data set potential for advancing research on activity and activity patterns and portfolios among older adults.
Results from their analysis showed that each data set contained multiple activity domains. Although no data set contained all of the identified domains, HRS and MIDUS contained most of them. Within each data set we reviewed, the number of activity items per domain varied. The specific activity items within domains also differed across data sets. Moreover, in cases where similar activities were inquired about (i.e., volunteering), often the measures themselves were not the same across data sets. One reason that there might be more activity items in a particular domain may be extensive triangulation of information in some data sets, such as the PSID, which rigorously measures employment so that fluctuations in employment and employment trends over time can be confidently investigated.

http://midus.wisc.edu/findings/pdfs/1313.pdf